Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations2199
Missing cells73
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory458.1 KiB
Average record size in memory213.3 B

Variable types

Text2
Numeric11

Alerts

Freedom to make life choices is highly overall correlated with Happiness score and 1 other fieldsHigh correlation
Generosity is highly overall correlated with GenerositysHigh correlation
Generositys is highly overall correlated with GenerosityHigh correlation
Happiness score is highly overall correlated with Freedom to make life choices and 4 other fieldsHigh correlation
Healthy life expectancy at birth is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Log GDP per capita is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Positive affect is highly overall correlated with Freedom to make life choices and 1 other fieldsHigh correlation
Social support is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Generosity has 73 (3.3%) missing values Missing
Healthy life expectancy at birth has 54 (2.5%) zeros Zeros
Freedom to make life choices has 33 (1.5%) zeros Zeros
Perceptions of corruption has 116 (5.3%) zeros Zeros
Positive affect has 24 (1.1%) zeros Zeros
Generositys has 80 (3.6%) zeros Zeros

Reproduction

Analysis started2025-03-18 07:40:14.748760
Analysis finished2025-03-18 07:40:29.210585
Duration14.46 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct165
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size140.3 KiB
2025-03-18T16:40:29.421475image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length25
Median length22
Mean length8.253297
Min length4

Characters and Unicode

Total characters18149
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
united 49
 
1.8%
china 43
 
1.6%
of 42
 
1.6%
south 37
 
1.4%
republic 22
 
0.8%
congo 22
 
0.8%
and 19
 
0.7%
chile 17
 
0.6%
bolivia 17
 
0.6%
bangladesh 17
 
0.6%
Other values (178) 2366
89.2%
2025-03-18T16:40:29.773263image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%
Distinct165
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size129.0 KiB
2025-03-18T16:40:30.031116image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.992724
Min length2

Characters and Unicode

Total characters6581
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG
ValueCountFrequency (%)
arg 17
 
0.8%
cri 17
 
0.8%
bra 17
 
0.8%
bol 17
 
0.8%
bgd 17
 
0.8%
col 17
 
0.8%
chl 17
 
0.8%
khm 17
 
0.8%
cmr 17
 
0.8%
can 17
 
0.8%
Other values (155) 2029
92.3%
2025-03-18T16:40:30.382915image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

year
Real number (ℝ)

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.1614
Minimum2005
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:30.463879image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12010
median2014
Q32018
95-th percentile2022
Maximum2022
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7187355
Coefficient of variation (CV)0.0023427792
Kurtosis-1.0716942
Mean2014.1614
Median Absolute Deviation (MAD)4
Skewness-0.076682854
Sum4429141
Variance22.266465
MonotonicityNot monotonic
2025-03-18T16:40:30.563812image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2017 147
 
6.7%
2011 146
 
6.6%
2014 144
 
6.5%
2019 143
 
6.5%
2015 142
 
6.5%
2012 141
 
6.4%
2018 141
 
6.4%
2016 141
 
6.4%
2013 136
 
6.2%
2010 124
 
5.6%
Other values (8) 794
36.1%
ValueCountFrequency (%)
2005 27
 
1.2%
2006 89
4.0%
2007 102
4.6%
2008 110
5.0%
2009 114
5.2%
2010 124
5.6%
2011 146
6.6%
2012 141
6.4%
2013 136
6.2%
2014 144
6.5%
ValueCountFrequency (%)
2022 114
5.2%
2021 122
5.5%
2020 116
5.3%
2019 143
6.5%
2018 141
6.4%
2017 147
6.7%
2016 141
6.4%
2015 142
6.5%
2014 144
6.5%
2013 136
6.2%

Happiness score
Real number (ℝ)

High correlation 

Distinct1713
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4792274
Minimum1.281
Maximum8.019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:30.686741image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1.281
5-th percentile3.6737
Q14.647
median5.432
Q36.3095
95-th percentile7.3765
Maximum8.019
Range6.738
Interquartile range (IQR)1.6625

Descriptive statistics

Standard deviation1.1255268
Coefficient of variation (CV)0.20541706
Kurtosis-0.5918227
Mean5.4792274
Median Absolute Deviation (MAD)0.825
Skewness-0.017831209
Sum12048.821
Variance1.2668105
MonotonicityNot monotonic
2025-03-18T16:40:30.823663image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.252 5
 
0.2%
4.64 4
 
0.2%
4.741 4
 
0.2%
5.304 4
 
0.2%
5.057 4
 
0.2%
5.887 4
 
0.2%
6.375 4
 
0.2%
3.476 3
 
0.1%
5.006 3
 
0.1%
4.683 3
 
0.1%
Other values (1703) 2161
98.3%
ValueCountFrequency (%)
1.281 1
< 0.1%
2.179 1
< 0.1%
2.352 1
< 0.1%
2.375 1
< 0.1%
2.436 1
< 0.1%
2.56 1
< 0.1%
2.634 1
< 0.1%
2.662 1
< 0.1%
2.688 1
< 0.1%
2.693 1
< 0.1%
ValueCountFrequency (%)
8.019 1
< 0.1%
7.971 1
< 0.1%
7.889 1
< 0.1%
7.858 1
< 0.1%
7.834 1
< 0.1%
7.794 1
< 0.1%
7.788 2
0.1%
7.78 1
< 0.1%
7.776 1
< 0.1%
7.771 1
< 0.1%

Log GDP per capita
Real number (ℝ)

High correlation 

Distinct1653
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3043602
Minimum0
Maximum11.664
Zeros20
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:30.966583image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.3249
Q18.4755
median9.492
Q310.366
95-th percentile10.9351
Maximum11.664
Range11.664
Interquartile range (IQR)1.8905

Descriptive statistics

Standard deviation1.4536807
Coefficient of variation (CV)0.15623651
Kurtosis13.158879
Mean9.3043602
Median Absolute Deviation (MAD)0.951
Skewness-2.4437227
Sum20460.288
Variance2.1131876
MonotonicityNot monotonic
2025-03-18T16:40:31.113499image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.9%
8.902 5
 
0.2%
10.878 5
 
0.2%
9.383 5
 
0.2%
10.714 5
 
0.2%
9.381 5
 
0.2%
9.283 5
 
0.2%
9.813 4
 
0.2%
10.788 4
 
0.2%
8.067 4
 
0.2%
Other values (1643) 2137
97.2%
ValueCountFrequency (%)
0 20
0.9%
5.527 1
 
< 0.1%
5.935 1
 
< 0.1%
5.943 1
 
< 0.1%
6.607 1
 
< 0.1%
6.687 1
 
< 0.1%
6.694 1
 
< 0.1%
6.699 1
 
< 0.1%
6.7 1
 
< 0.1%
6.707 1
 
< 0.1%
ValueCountFrequency (%)
11.664 1
< 0.1%
11.66 1
< 0.1%
11.653 1
< 0.1%
11.649 1
< 0.1%
11.647 1
< 0.1%
11.645 1
< 0.1%
11.638 1
< 0.1%
11.637 1
< 0.1%
11.636 1
< 0.1%
11.635 1
< 0.1%

Social support
Real number (ℝ)

High correlation 

Distinct478
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80588859
Minimum0
Maximum0.987
Zeros13
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:31.259415image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5568
Q10.744
median0.834
Q30.905
95-th percentile0.951
Maximum0.987
Range0.987
Interquartile range (IQR)0.161

Descriptive statistics

Standard deviation0.13567265
Coefficient of variation (CV)0.16835163
Kurtosis6.8965788
Mean0.80588859
Median Absolute Deviation (MAD)0.076
Skewness-1.9447968
Sum1772.149
Variance0.018407069
MonotonicityNot monotonic
2025-03-18T16:40:31.395346image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.937 17
 
0.8%
0.917 15
 
0.7%
0.818 15
 
0.7%
0.866 15
 
0.7%
0.878 15
 
0.7%
0.904 14
 
0.6%
0.909 14
 
0.6%
0.91 14
 
0.6%
0.863 14
 
0.6%
0.926 14
 
0.6%
Other values (468) 2052
93.3%
ValueCountFrequency (%)
0 13
0.6%
0.228 1
 
< 0.1%
0.29 1
 
< 0.1%
0.291 2
 
0.1%
0.303 1
 
< 0.1%
0.32 1
 
< 0.1%
0.326 1
 
< 0.1%
0.366 1
 
< 0.1%
0.373 1
 
< 0.1%
0.382 1
 
< 0.1%
ValueCountFrequency (%)
0.987 1
< 0.1%
0.985 2
0.1%
0.984 1
< 0.1%
0.983 2
0.1%
0.982 2
0.1%
0.98 2
0.1%
0.979 2
0.1%
0.977 2
0.1%
0.976 1
< 0.1%
0.975 1
< 0.1%

Healthy life expectancy at birth
Real number (ℝ)

High correlation  Zeros 

Distinct1108
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.740281
Minimum0
Maximum74.475
Zeros54
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:31.525273image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48.794
Q158.31
median64.88
Q368.33
95-th percentile71.6
Maximum74.475
Range74.475
Interquartile range (IQR)10.02

Descriptive statistics

Standard deviation11.935737
Coefficient of variation (CV)0.19332172
Kurtosis15.207288
Mean61.740281
Median Absolute Deviation (MAD)4.56
Skewness-3.4897441
Sum135766.88
Variance142.46182
MonotonicityNot monotonic
2025-03-18T16:40:31.659185image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
2.5%
66.6 16
 
0.7%
70 16
 
0.7%
65.8 16
 
0.7%
67 12
 
0.5%
67.5 12
 
0.5%
70.9 10
 
0.5%
65.7 10
 
0.5%
67.6 10
 
0.5%
66.3 9
 
0.4%
Other values (1098) 2034
92.5%
ValueCountFrequency (%)
0 54
2.5%
6.72 1
 
< 0.1%
17.36 1
 
< 0.1%
28 1
 
< 0.1%
33.32 1
 
< 0.1%
38.64 1
 
< 0.1%
40.4 1
 
< 0.1%
41.48 1
 
< 0.1%
41.52 1
 
< 0.1%
41.6 1
 
< 0.1%
ValueCountFrequency (%)
74.475 1
< 0.1%
74.35 1
< 0.1%
74.225 1
< 0.1%
74.1 1
< 0.1%
73.975 1
< 0.1%
73.925 1
< 0.1%
73.85 1
< 0.1%
73.8 1
< 0.1%
73.725 1
< 0.1%
73.65 1
< 0.1%

Freedom to make life choices
Real number (ℝ)

High correlation  Zeros 

Distinct545
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73662437
Minimum0
Maximum0.985
Zeros33
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:31.795108image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4549
Q10.65
median0.767
Q30.858
95-th percentile0.935
Maximum0.985
Range0.985
Interquartile range (IQR)0.208

Descriptive statistics

Standard deviation0.16617585
Coefficient of variation (CV)0.22559103
Kurtosis4.1998612
Mean0.73662437
Median Absolute Deviation (MAD)0.103
Skewness-1.5627044
Sum1619.837
Variance0.027614414
MonotonicityNot monotonic
2025-03-18T16:40:31.940027image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
1.5%
0.838 13
 
0.6%
0.891 11
 
0.5%
0.817 11
 
0.5%
0.905 11
 
0.5%
0.882 10
 
0.5%
0.733 10
 
0.5%
0.904 10
 
0.5%
0.878 10
 
0.5%
0.773 10
 
0.5%
Other values (535) 2070
94.1%
ValueCountFrequency (%)
0 33
1.5%
0.258 1
 
< 0.1%
0.26 1
 
< 0.1%
0.281 1
 
< 0.1%
0.287 1
 
< 0.1%
0.295 1
 
< 0.1%
0.304 1
 
< 0.1%
0.306 1
 
< 0.1%
0.315 1
 
< 0.1%
0.332 1
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.98 1
 
< 0.1%
0.975 1
 
< 0.1%
0.971 1
 
< 0.1%
0.97 3
0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%
0.965 2
0.1%
0.964 2
0.1%

Generosity
Real number (ℝ)

High correlation  Missing 

Distinct625
Distinct (%)29.4%
Missing73
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean9.1251176 × 10-5
Minimum-0.338
Maximum0.703
Zeros7
Zeros (%)0.3%
Negative1187
Negative (%)54.0%
Memory size17.3 KiB
2025-03-18T16:40:32.075947image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-0.338
5-th percentile-0.229
Q1-0.112
median-0.023
Q30.092
95-th percentile0.29775
Maximum0.703
Range1.041
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.16107902
Coefficient of variation (CV)1765.2268
Kurtosis0.83127602
Mean9.1251176 × 10-5
Median Absolute Deviation (MAD)0.102
Skewness0.77703862
Sum0.194
Variance0.025946452
MonotonicityNot monotonic
2025-03-18T16:40:32.219865image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.027 11
 
0.5%
-0.063 11
 
0.5%
-0.019 11
 
0.5%
-0.023 10
 
0.5%
-0.069 10
 
0.5%
0.05 10
 
0.5%
-0.04 10
 
0.5%
-0.011 10
 
0.5%
-0.047 10
 
0.5%
-0.081 10
 
0.5%
Other values (615) 2023
92.0%
(Missing) 73
 
3.3%
ValueCountFrequency (%)
-0.338 1
< 0.1%
-0.319 1
< 0.1%
-0.316 1
< 0.1%
-0.31 1
< 0.1%
-0.309 1
< 0.1%
-0.308 1
< 0.1%
-0.307 1
< 0.1%
-0.306 1
< 0.1%
-0.3 1
< 0.1%
-0.299 1
< 0.1%
ValueCountFrequency (%)
0.703 1
< 0.1%
0.695 1
< 0.1%
0.694 1
< 0.1%
0.683 1
< 0.1%
0.654 1
< 0.1%
0.649 1
< 0.1%
0.563 1
< 0.1%
0.552 1
< 0.1%
0.551 1
< 0.1%
0.543 1
< 0.1%

Perceptions of corruption
Real number (ℝ)

Zeros 

Distinct602
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70589768
Minimum0
Maximum0.983
Zeros116
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:32.359785image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6565
median0.791
Q30.866
95-th percentile0.9381
Maximum0.983
Range0.983
Interquartile range (IQR)0.2095

Descriptive statistics

Standard deviation0.24591453
Coefficient of variation (CV)0.34837135
Kurtosis1.7705347
Mean0.70589768
Median Absolute Deviation (MAD)0.093
Skewness-1.595041
Sum1552.269
Variance0.060473956
MonotonicityNot monotonic
2025-03-18T16:40:32.501704image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116
 
5.3%
0.844 16
 
0.7%
0.884 14
 
0.6%
0.755 13
 
0.6%
0.743 13
 
0.6%
0.868 13
 
0.6%
0.841 13
 
0.6%
0.848 12
 
0.5%
0.849 12
 
0.5%
0.863 12
 
0.5%
Other values (592) 1965
89.4%
ValueCountFrequency (%)
0 116
5.3%
0.035 1
 
< 0.1%
0.047 1
 
< 0.1%
0.06 1
 
< 0.1%
0.064 1
 
< 0.1%
0.066 1
 
< 0.1%
0.07 1
 
< 0.1%
0.078 1
 
< 0.1%
0.081 1
 
< 0.1%
0.095 1
 
< 0.1%
ValueCountFrequency (%)
0.983 2
0.1%
0.979 1
 
< 0.1%
0.977 2
0.1%
0.976 2
0.1%
0.974 1
 
< 0.1%
0.973 2
0.1%
0.97 2
0.1%
0.969 1
 
< 0.1%
0.968 3
0.1%
0.967 3
0.1%

Positive affect
Real number (ℝ)

High correlation  Zeros 

Distinct436
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64503001
Minimum0
Maximum0.884
Zeros24
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:32.638636image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.469
Q10.568
median0.662
Q30.737
95-th percentile0.803
Maximum0.884
Range0.884
Interquartile range (IQR)0.169

Descriptive statistics

Standard deviation0.12525224
Coefficient of variation (CV)0.19418048
Kurtosis6.0654022
Mean0.64503001
Median Absolute Deviation (MAD)0.082
Skewness-1.6326098
Sum1418.421
Variance0.015688124
MonotonicityNot monotonic
2025-03-18T16:40:32.774558image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
1.1%
0.718 15
 
0.7%
0.699 14
 
0.6%
0.689 13
 
0.6%
0.74 13
 
0.6%
0.742 12
 
0.5%
0.702 12
 
0.5%
0.583 12
 
0.5%
0.658 12
 
0.5%
0.717 12
 
0.5%
Other values (426) 2060
93.7%
ValueCountFrequency (%)
0 24
1.1%
0.179 1
 
< 0.1%
0.206 1
 
< 0.1%
0.263 1
 
< 0.1%
0.297 1
 
< 0.1%
0.298 1
 
< 0.1%
0.308 1
 
< 0.1%
0.324 1
 
< 0.1%
0.332 1
 
< 0.1%
0.347 1
 
< 0.1%
ValueCountFrequency (%)
0.884 1
 
< 0.1%
0.876 1
 
< 0.1%
0.874 1
 
< 0.1%
0.86 1
 
< 0.1%
0.853 1
 
< 0.1%
0.851 1
 
< 0.1%
0.849 1
 
< 0.1%
0.847 1
 
< 0.1%
0.844 1
 
< 0.1%
0.841 5
0.2%

Negative affect
Real number (ℝ)

Distinct391
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26951796
Minimum0
Maximum0.705
Zeros16
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T16:40:32.902486image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.147
Q10.207
median0.26
Q30.3225
95-th percentile0.43
Maximum0.705
Range0.705
Interquartile range (IQR)0.1155

Descriptive statistics

Standard deviation0.089578858
Coefficient of variation (CV)0.33236693
Kurtosis0.96011836
Mean0.26951796
Median Absolute Deviation (MAD)0.056
Skewness0.52501697
Sum592.67
Variance0.0080243717
MonotonicityNot monotonic
2025-03-18T16:40:33.038407image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.206 19
 
0.9%
0 16
 
0.7%
0.232 16
 
0.7%
0.24 16
 
0.7%
0.26 15
 
0.7%
0.226 15
 
0.7%
0.218 15
 
0.7%
0.233 15
 
0.7%
0.276 15
 
0.7%
0.268 15
 
0.7%
Other values (381) 2042
92.9%
ValueCountFrequency (%)
0 16
0.7%
0.083 2
 
0.1%
0.093 2
 
0.1%
0.094 1
 
< 0.1%
0.095 2
 
0.1%
0.1 1
 
< 0.1%
0.103 1
 
< 0.1%
0.106 1
 
< 0.1%
0.107 1
 
< 0.1%
0.108 3
 
0.1%
ValueCountFrequency (%)
0.705 1
< 0.1%
0.643 1
< 0.1%
0.622 1
< 0.1%
0.607 1
< 0.1%
0.599 1
< 0.1%
0.591 1
< 0.1%
0.581 1
< 0.1%
0.576 1
< 0.1%
0.57 1
< 0.1%
0.569 1
< 0.1%

Generositys
Real number (ℝ)

High correlation  Zeros 

Distinct625
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8221919 × 10-5
Minimum-0.338
Maximum0.703
Zeros80
Zeros (%)3.6%
Negative1187
Negative (%)54.0%
Memory size17.3 KiB
2025-03-18T16:40:33.174330image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-0.338
5-th percentile-0.228
Q1-0.107
median-0.017
Q30.087
95-th percentile0.296
Maximum0.703
Range1.041
Interquartile range (IQR)0.194

Descriptive statistics

Standard deviation0.15838156
Coefficient of variation (CV)1795.2631
Kurtosis0.96286671
Mean8.8221919 × 10-5
Median Absolute Deviation (MAD)0.098
Skewness0.79030542
Sum0.194
Variance0.025084718
MonotonicityNot monotonic
2025-03-18T16:40:33.309252image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
3.6%
-0.063 11
 
0.5%
-0.027 11
 
0.5%
-0.019 11
 
0.5%
-0.023 10
 
0.5%
0.05 10
 
0.5%
-0.069 10
 
0.5%
-0.011 10
 
0.5%
-0.081 10
 
0.5%
-0.047 10
 
0.5%
Other values (615) 2026
92.1%
ValueCountFrequency (%)
-0.338 1
< 0.1%
-0.319 1
< 0.1%
-0.316 1
< 0.1%
-0.31 1
< 0.1%
-0.309 1
< 0.1%
-0.308 1
< 0.1%
-0.307 1
< 0.1%
-0.306 1
< 0.1%
-0.3 1
< 0.1%
-0.299 1
< 0.1%
ValueCountFrequency (%)
0.703 1
< 0.1%
0.695 1
< 0.1%
0.694 1
< 0.1%
0.683 1
< 0.1%
0.654 1
< 0.1%
0.649 1
< 0.1%
0.563 1
< 0.1%
0.552 1
< 0.1%
0.551 1
< 0.1%
0.543 1
< 0.1%

Interactions

2025-03-18T16:40:27.502571image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:14.987715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:16.927604image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.076957image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.206312image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.399621image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:21.883771image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.004134image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.080518image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.204885image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.280269image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:27.605512image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:15.096653image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.037553image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.188894image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.311242image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.515565image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:21.991711image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.106083image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.189464image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.309826image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.391206image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:27.707446image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:15.215585image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.142484image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.291826image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.417181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.626501image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.096660image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.201029image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.292397image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.407768image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.510140image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:27.805388image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:15.333516image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.248432image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.390779image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.523124image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.733440image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.201591image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.298965image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.396339image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.504712image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.621074image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:28.242139image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:15.453449image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.350365image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.493720image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.619068image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.856361image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.296547image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.407901image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.500279image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.600659image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.728014image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:28.347088image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:15.578376image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.464308image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.602664image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.734001image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.964298image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.408473image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.506854image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.607216image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.710596image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.849936image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:28.448031image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:15.692321image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.572247image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.703599image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.832944image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:21.072246image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.504427image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.601790image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.717153image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.806541image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.958873image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:28.537979image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:16.495864image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.666183image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.797547image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.926900image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:21.171181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.599364image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.689739image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.813108image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.898488image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:27.070818image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:28.635923image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:16.603799image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.769123image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:18.901486image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.081803image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:21.281127image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.696318image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.799687image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:24.909045image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.997431image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:27.178758image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:28.727872image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:16.707741image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.868069image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.002430image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.182745image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:21.385057image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.791263image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.888636image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.000991image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.090378image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:27.287693image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:28.825806image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:16.817669image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:17.975019image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:19.110367image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:20.290692image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:21.780843image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:22.903189image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:23.988579image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:25.111928image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:26.189321image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T16:40:27.399630image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-18T16:40:33.411193image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Freedom to make life choicesGenerosityGenerositysHappiness scoreHealthy life expectancy at birthLog GDP per capitaNegative affectPerceptions of corruptionPositive affectSocial supportyear
Freedom to make life choices1.0000.3460.3380.5300.3850.397-0.228-0.4130.5680.4450.232
Generosity0.3461.0001.0000.1630.0140.005-0.081-0.1990.3070.0890.011
Generositys0.3381.0001.0000.1650.0160.010-0.081-0.1980.3000.0890.007
Happiness score0.5300.1630.1651.0000.7280.795-0.291-0.3180.5050.7470.068
Healthy life expectancy at birth0.3850.0140.0160.7281.0000.799-0.112-0.2170.2660.6200.141
Log GDP per capita0.3970.0050.0100.7950.7991.000-0.250-0.2700.2470.6990.072
Negative affect-0.228-0.081-0.081-0.291-0.112-0.2501.0000.201-0.257-0.4060.203
Perceptions of corruption-0.413-0.199-0.198-0.318-0.217-0.2700.2011.000-0.228-0.188-0.109
Positive affect0.5680.3070.3000.5050.2660.247-0.257-0.2281.0000.4180.038
Social support0.4450.0890.0890.7470.6200.699-0.406-0.1880.4181.000-0.011
year0.2320.0110.0070.0680.1410.0720.203-0.1090.038-0.0111.000

Missing values

2025-03-18T16:40:28.977719image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-18T16:40:29.122645image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Country nameIso alphayearHappiness scoreLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affectGenerositys
0AfghanistanAFG20083.727.350.4550.500.720.170.880.410.260.17
1AfghanistanAFG20094.407.510.5550.800.680.190.850.480.240.19
2AfghanistanAFG20104.767.610.5451.100.600.120.710.520.280.12
3AfghanistanAFG20113.837.580.5251.400.500.160.730.480.270.16
4AfghanistanAFG20123.787.660.5251.700.530.240.780.610.270.24
5AfghanistanAFG20133.577.680.4852.000.580.060.820.550.270.06
6AfghanistanAFG20143.137.670.5352.300.510.110.870.490.380.11
7AfghanistanAFG20153.987.650.5352.600.390.080.880.490.340.08
8AfghanistanAFG20164.227.650.5652.920.520.040.790.500.350.04
9AfghanistanAFG20172.667.650.4953.250.43-0.120.950.430.37-0.12
Country nameIso alphayearHappiness scoreLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affectGenerositys
2189ZimbabweZWE20134.697.750.8048.800.58-0.090.830.620.18-0.09
2190ZimbabweZWE20144.187.750.7750.000.64-0.060.820.660.24-0.06
2191ZimbabweZWE20153.707.750.7451.200.67-0.110.810.640.18-0.11
2192ZimbabweZWE20163.737.740.7751.670.73-0.080.720.690.21-0.08
2193ZimbabweZWE20173.647.750.7552.150.75-0.080.750.730.22-0.08
2194ZimbabweZWE20183.627.780.7852.620.76-0.050.840.660.21-0.05
2195ZimbabweZWE20192.697.700.7653.100.63-0.050.830.660.23-0.05
2196ZimbabweZWE20203.167.600.7253.580.640.010.790.660.350.01
2197ZimbabweZWE20213.157.660.6954.050.67-0.080.760.610.24-0.08
2198ZimbabweZWE20223.307.670.6754.520.65-0.070.750.640.19-0.07